Sofia Moco

Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry

Untargeted metabolomics aims to gather information on as many metabolites as possible in biological systems by taking into account all information present in the data sets. Here we describe a detailed protocol for large-scale untargeted metabolomics of plant tissues, based on reversed phase liquid chromatography coupled to high-resolution mass spectrometry (LC-QTOF MS) of aqueous methanol extracts. Dedicated software, MetAlign, is used for automated baseline correction and alignment of all extracted mass peaks across all samples, producing detailed information on the relative abundance of thousands of mass signals representing hundreds of metabolites. Subsequent statistics and bioinformatics tools can be used to provide a detailed view on the differences and similarities between (groups of) samples or to link metabolomics data to other systems biology information, genetic markers and/or specific quality parameters. The complete procedure from metabolite extraction to assembly of a data matrix with aligned mass signal intensities takes about 6 days for 50 samples.

A liquid chromatography-mass spectrometry-based metabolome database for tomato.

For the description of the metabolome of an organism, the development of common metabolite databases is of utmost importance. Here we present the Metabolome Tomato Database (MoTo DB), a metabolite database dedicated to liquid chromatography-mass spectrometry (LC-MS)- based metabolomics of tomato fruit (Solanum lycopersicum). A reproducible analytical approach consisting of reversed-phase LC coupled to quadrupole time-of-flight MS and photodiode array detection (PDA) was developed for large-scale detection and identification of mainly semipolar metabolites in plants and for the incorporation of the tomato fruit metabolite data into the MoTo DB. Chromatograms were processed using software tools for mass signal extraction and alignment, and intensity-dependent accurate mass calculation. The detected masses were assigned by matching their accurate mass signals with tomato compounds reported in literature and complemented, as much as possible, by PDA and MS/MS information, as well as by using reference compounds. Several novel compounds not previously reported for tomato fruit were identified in this manner and added to the database. The MoTo DB is available at http://appliedbioinformatics.wur.nl and contains all information so far assembled using this LC-PDA-quadrupole time-of-flight MS platform, including retention times, calculated accurate masses, PDA spectra, MS/MS fragments, and literature references. Unbiased metabolic profiling and comparison of peel and flesh tissues from tomato fruits validated the applicability of the MoTo DB, revealing that all flavonoids and α-tomatine were specifically present in the peel, while several other alkaloids and some particular phenylpropanoids were mainly present in the flesh tissue.

Ultrahigh performance liquid chromatography-tandem mass spectrometry method for fast and robust quantification of anionic and aromatic metabolites.

Quantification of metabolites is of pivotal relevance in biology, where it complements more established omics techniques such as transcriptomics and proteomics. Here, we present a 25 min ion-pairing ultrahigh performance liquid chromatography-tandem mass spectrometry method that was developed for comprehensive coverage of central metabolism (glycolysis, pentose phosphate pathway, and tricarboxylic acid cycle) and closely related biosynthetic reactions. We demonstrate quantification of 138 compounds, including carboxylic acids, amino acids, sugar phosphates, nucleotides, and functionalized aromatics. Biologically relevant isomers such as sugar phosphates are individually quantified by combining chromatographic separation and fragmentation. The obtained sensitivity and robustness enabled the detection of more than half all tested compounds in each of eight diverse biological samples of 0.5-50 mg dry cell weight. We recommend this method for routine and yet comprehensive quantification of primary metabolites in a wide variety of biological matrices.

Metabolomics View on Gut Microbiome Modulation by Polyphenol-rich Foods

Health is influenced by genetic, lifestyle, and diet determinants; therefore, nutrition plays an essential role in health management. Still, the substantiation of nutritional health benefits is challenged by the intrinsic macro- and micronutrient complexity of foods and individual responses. Evidence of healthy effects of food requires new strategies not only to stratify populations according to their metabolic requirements but also to predict and measure individual responses to dietary intakes. The influence of the gut microbiome and its interaction with the host is pivotal to understand nutrition and metabolism. Thus, the modulation of the gut microbiome composition by alteration of food habits has potentialities in health improvement or even disease prevention. Dietary polyphenols are naturally occurring constituents in vegetables and fruits, including coffee and cocoa. They are commonly associated to health benefits, although mechanistic evidence in vivo is not yet fully understood. Polyphenols are extensively metabolized by gut bacteria into a complex series of end-products that support a significant effect on the functional ecology of symbiotic partners that can affect the host physiology. This review reports recent nutritional metabolomics inspections of gut microbiota-host metabolic interactions with a particular focus on the cometabolism of cocoa and coffee polyphenols.

Intra- and inter-metabolite correlation spectroscopy of tomato metabolomics data obtained by liquid chromatography-mass spectrometry and nuclear magnetic resonance

Nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LCMS) are frequently used as technological platforms for metabolomics applications. In this study, the metabolic profiles of ripe fruits from 50 different tomato cultivars, including beef, cherry and round types, were recorded by both 1H NMR and accurate mass LC-quadrupole time-of-flight (QTOF) MS. Different analytical selectivities were found for these both profiling techniques. In fact, NMR and LCMS provided complementary data, as the metabolites detected belong to essentially different metabolic pathways. Yet, upon unsupervised multivariate analysis, both NMR and LCMS datasets revealed a clear segregation of, on the one hand, the cherry tomatoes and, on the other hand, the beef and round tomatoes. Intra-method (NMR–NMR, LCMS–LCMS) and inter-method (NMR–LCMS) correlation analyses were performed enabling the annotation of metabolites from highly correlating metabolite signals. Signals belonging to the same metabolite or to chemically related metabolites are among the highest correlations found. Inter-method correlation analysis produced highly informative and complementary information for the identification of metabolites, even in de case of low abundant NMR signals. The applied approach appears to be a promising strategy in extending the analytical capacities of these metabolomics techniques with regard to the discovery and identification of biomarkers and yet unknown metabolites.

Metabolomics perspectives in pediatric research

Increasing evidence points toward the critical and long-term involvement of prenatal and early nutrition and lifestyle on later health and disease risk predisposition. Metabolomics is now a well-established top–down systems biology approach that explores the genetic–environment–health paradigm. The generalization of such approaches has opened new research areas to deepen our current understanding of many physiological processes, as well as foods and nutrient functionalities in target populations. It is envisioned that this will provide new avenues toward preventive medicine and prognostic strategies for tailored therapeutic and personalized nutrition management. The development of systems biology approaches and the new generation of biomarker patterns will provide the opportunity to associate complex metabolic regulations with the etiology of multifactorial pediatric diseases. This may subsequently lead to the development of system mechanistic hypotheses that could be targeted with new nutritional personalized concepts. Therefore, this review aims to describe recent applications of metabolomics in preclinical and clinical fields with insights into disease diagnostics/monitoring and improvement of homeostasis metabolic regulation that may be translatable to novel therapeutic and nutrition advances in pediatric research.

A Whole-Grain–Rich Diet Reduces Urinary Excretion of Markers of Protein Catabolism and Gut Microbiota Metabolism in Healthy Men after One Week

Epidemiological studies consistently find that diets rich in whole-grain (WG) cereals lead to decreased risk of disease compared with refined grain (RG)-based diets. Aside from a greater amount of fiber and micronutrients, possible mechanisms for why WGs may be beneficial for health remain speculative. In an exploratory, randomized, researcher-blinded, crossover trial, we measured metabolic profile differences between healthy participants eating a diet based on WGs compared with a diet based on RGs. Seventeen healthy adult participants (11 female, 6 male) consumed a controlled diet based on either WG-rich or RG-rich foods for 2 wk, followed by the other diet after a 5-wk washout period. Both diets were the same except for the use of WG (150 g/d) or RG foods. The metabolic profiles of plasma, urine, and fecal water were measured using (1)H-nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry (plasma only). After 1 wk of intervention, the WG diet led to decreases in urinary excretion of metabolites related to protein catabolism (urea, methylguanadine), lipid (carnitine and acylcarnitines) and gut microbial (4-hydroxyphenylacetate, trimethylacetate, dimethylacetate) metabolism in men compared with the same time point during the RG intervention. There were no differences between the interventions after 2 wk. Urinary urea, carnitine, and acylcarnitine were lower at wk 1 of the WG intervention relative to the RG intervention in all participants. Fecal water short-chain fatty acids acetate and butyrate were relatively greater after the WG diet compared to the RG diet. Although based on a small population and for a short time period, these observations suggest that a WG diet may affect protein metabolism.

Specific Dietary Preferences Are Linked to Differing Gut Microbial Metabolic Activity in Response to Dark Chocolate Intake

Systems biology approaches are providing novel insights into the role of nutrition for the management of health and disease. In the present study, we investigated if dietary preference for dark chocolate in healthy subjects may lead to different metabolic response to daily chocolate consumption. Using NMR- and MS-based metabolic profiling of blood plasma and urine, we monitored the metabolic response of 10 participants stratified as chocolate desiring and eating regularly dark chocolate (CD) and 10 participants stratified as chocolate indifferent and eating rarely dark chocolate (CI) to a daily consumption of 50 g of dark chocolate as part of a standardized diet over a one week period. We demonstrated that preference for chocolate leads to different metabolic response to chocolate consumption. Daily intake of dark chocolate significantly increased HDL cholesterol by 6% and decreased polyunsaturated acyl ether phospholipids. Dark chocolate intake could also induce an improvement in the metabolism of long chain fatty acid, as noted by a compositional change in plasma fatty acyl carnitines. Moreover, a relationship between regular long-term dietary exposure to a small amount of dark chocolate, gut microbiota, and phenolics was highlighted, providing novel insights into biological processes associated with cocoa bioactives.

Recombinant expression and functional characterisation of regiospecific flavonoid glucosyltransferases from Hieracium pilosella L.

Five glucosyltransferases were cloned by RT-PCR amplification using total RNA from Hieraciumpilosella L. (Asteraceae) inflorescences as template. Expression was accomplished in Escherichia coli, and three of the HIS-tagged enzymes, UGT90A7, UGT95A1, and UGT72B11 were partially purified and functionally characterised as UDP-glucose:flavonoid O-glucosyltransferases. Both UGT90A7 and UGT95A1 preferred luteolin as substrate, but possessed different regiospecificity profiles. UGT95A1 established a new subgroup within the UGT family showing high regiospecificity towards the C-3′ hydroxyl group of luteolin, while UGT90A7 primarily yielded the 4′-O-glucoside, but concomitantly catalysed also the formation of the 7-O-glucoside, which could account for this flavones glucoside in H. pilosella flower heads. Semi quantitative expression profiles revealed that UGT95A1 was expressed at all stages of inflorescence development as well as in leaf and stem tissue, whereas UGT90A7 transcript abundance was nearly limited to flower tissue and started to develop with the pigmentation of closed buds. Other than these enzymes, UGT72B11 showed rather broad substrate acceptance, with highest activity towards flavones and flavonols which have not been reported from H. pilosella. As umbelliferone was also readily accepted, this enzyme could be involved in the glucosylation of coumarins and other metabolites.

Impact of breast-feeding and high- and low-protein formula on the metabolism and growth of infants from overweight and obese mothers

Background:The combination of maternal obesity in early pregnancy and high protein intake in infant formula feeding might predispose to obesity risk in later life.Methods:This study assesses the impact of breast- or formula-feeding (differing in protein content by 1.65 or 2.7 g/100 kcal) on the metabolism of term infants from overweight and obese mothers. From birth to 3 mo of age, infants received exclusively either breast- or starter formula-feeding and until 6 mo, exclusively either a formula designed for this study or breast-feeding. From 6 to 12 mo, infants received complementary weaning food. Metabonomics was conducted on the infants’ urine and stool samples collected at the age of 3, 6, and 12 mo.Results:Infant formula-feeding resulted in higher protein-derived short-chain fatty acids and amino acids in stools. Urine metabonomics revealed a relationship between bacterial processing of dietary proteins and host protein metabolism stimulated with increasing protein content in the formula. Moreover, formula-fed infants were metabolically different from breast-fed infants, at the level of lipid and energy metabolism (carnitines, ketone bodies, and Krebs cycle).Conclusion:Noninvasive urine and stool metabolic monitoring of responses to early nutrition provides relevant readouts to assess nutritional requirements for infants’ growth.